Model parameter estimation and analysis: Understanding parametric structure

Hsuehmin Li, Karen Watanabe-Sailor, David Auslander, Robert C. Spear

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

Original languageEnglish (US)
Pages (from-to)97-111
Number of pages15
JournalAnnals of Biomedical Engineering
Volume22
Issue number1
DOIs
StatePublished - Jan 1994
Externally publishedYes

Fingerprint

Parameter estimation
Pharmacokinetics
Clustering algorithms
Structural analysis
Benzene

Keywords

  • Benzene
  • Monte Carlo simulations
  • Parameter estimation
  • Parametric analysis
  • Pharmacokinetic models

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Model parameter estimation and analysis : Understanding parametric structure. / Li, Hsuehmin; Watanabe-Sailor, Karen; Auslander, David; Spear, Robert C.

In: Annals of Biomedical Engineering, Vol. 22, No. 1, 01.1994, p. 97-111.

Research output: Contribution to journalArticle

Li, Hsuehmin ; Watanabe-Sailor, Karen ; Auslander, David ; Spear, Robert C. / Model parameter estimation and analysis : Understanding parametric structure. In: Annals of Biomedical Engineering. 1994 ; Vol. 22, No. 1. pp. 97-111.
@article{fdac3938af2c4d9ab0fda3b351b748fd,
title = "Model parameter estimation and analysis: Understanding parametric structure",
abstract = "We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.",
keywords = "Benzene, Monte Carlo simulations, Parameter estimation, Parametric analysis, Pharmacokinetic models",
author = "Hsuehmin Li and Karen Watanabe-Sailor and David Auslander and Spear, {Robert C.}",
year = "1994",
month = "1",
doi = "10.1007/BF02368226",
language = "English (US)",
volume = "22",
pages = "97--111",
journal = "Annals of Biomedical Engineering",
issn = "0090-6964",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Model parameter estimation and analysis

T2 - Understanding parametric structure

AU - Li, Hsuehmin

AU - Watanabe-Sailor, Karen

AU - Auslander, David

AU - Spear, Robert C.

PY - 1994/1

Y1 - 1994/1

N2 - We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

AB - We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

KW - Benzene

KW - Monte Carlo simulations

KW - Parameter estimation

KW - Parametric analysis

KW - Pharmacokinetic models

UR - http://www.scopus.com/inward/record.url?scp=0028292250&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028292250&partnerID=8YFLogxK

U2 - 10.1007/BF02368226

DO - 10.1007/BF02368226

M3 - Article

C2 - 8060031

AN - SCOPUS:0028292250

VL - 22

SP - 97

EP - 111

JO - Annals of Biomedical Engineering

JF - Annals of Biomedical Engineering

SN - 0090-6964

IS - 1

ER -